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Automatika
Journal for Control, Measurement, Electronics, Computing and
Communications
ISSN: 0005-1144 (Print) 1848-3380 (Online) Journal homepage: https://www.tandfonline.com/loi/taut20
Dynamic virtual cluster cloud security using hybrid
steganographic image authentication algorithm
K. Venkatraman & K. Geetha
To cite this article: K. Venkatraman & K. Geetha (2019) Dynamic virtual cluster cloud security
using hybrid steganographic image authentication algorithm, Automatika, 60:3, 314-321, DOI: 10.1080/00051144.2019.1624409
To link to this article: https://doi.org/10.1080/00051144.2019.1624409
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
Published online: 12 Jun 2019.
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2019, VOL. 60, NO. 3, 314–321
https://doi.org/10.1080/00051144.2019.1624409
SPECIAL ISSUE: COMPUTATIONAL INTELLIGENCE AND CAPSULE NETWORKS
Dynamic virtual cluster cloud security using hybrid steganographic image
authentication algorithm
K. Venkatramanaand K. Geethab
aAnna University, Chennai, India;bDepartment of Computer Science & Engineering, Excel Engineering College, Anna University, Komarapalayam, India
ABSTRACT
Storing data in a third party cloud system causes serious problems on data confidentiality. Generally, encryption techniques provide data confidentiality but with limited functionality, which occurs due to unsupported actions of encryption operation in cloud storage space. Hence, developing a decentralized secure storage system with multiple support functions like encryp-tion, encoding, and forwarding tends to get complicated, when the storage system spreads. This paper aims mainly on hiding image information using specialized steganographic image authen-tication (SSIA) algorithm in clustered cloud systems. The SSIA algorithm is applied to virtual elastic clusters in a public cloud platform. Here, the SSIA algorithm embeds the image infor-mation using blowfish algorithm and genetic operators. Initially, the blowfish symmetric block encryption is applied over the image and then the genetic operator is applied to re-encrypt the image information. The proposed algorithm provides an improved security than conventional blowfish algorithm in a clustered cloud system.
ARTICLE HISTORY Received 18 January 2019 Accepted 29 April 2019 KEYWORDS Genetic algorithm; encryption; blowfish algorithm; cloud security; steganography
1. Introduction
In recent years, cloud computing gained a better recog-nition in organization and individuals using storage, computation, and software services [1]. The cloud computing faces multiple challenges, some include auditability, lock-in, transfer problems, confidentiality, unpredictability, performance, storage scalability, bugs, and software licensing [2]. The main risk in cloud com-puting is its privacy, interoperability, and compatibility [3]. The owner is unaware to control the data pri-vacy and the issues of security and pripri-vacy related to data integrity, availability of service, and intrusion in data [4].
The main drawback associated with cloud comput-ing is its issues related to security and privacy of data. Since, cloud computing is an important application in health, banking, and security services [5–7], the data is considered sensitive. Hence, better confidentiality has to be maintained at the user and server side during pro-cessing, rest, and transfer. This has imposed the present study to consider data security as an important and critical issue that has to be addressed thoroughly. The present research deals with analysing the security using steganographic encryption procedure in cluster-based cloud data model.
In the proposed system, the cloud computing secu-rity is improved using secured cryptosystem. The mod-ules of steganography security are different for cloud
system and general systems, especially in distributed computing. The main feature of the steganography model is that it is possible to make the model work under various sites of the distributed system [8]. For cloud computing, the attacks on steganography oper-ation relate to side channel attacks. To make the steganography algorithm to operate on cloud, the cloud system should provide better elastic services and bet-ter support of the steganography algorithm on a cloud environment.
The cryptosystem used is steganography based sys-tem, which is designed to work on the distributed data and further, it accumulates and reduces the data loss with increased efficiency without any security loss. The efficiency of the proposed system is computed on both high-performance computation and usage of valuable resource while designing key and random sequence. Careful measures are considered in the proposed study and the proposed method is modelled with machine learning steganography approaches.
In this paper, a domain-specific model is proposed to encrypt the images. The major contributions of this work is discussed as follows:
(i) The blowfish algorithm is used for steganographic encryption and genetic algorithm is used to encrypt the image further using crossover and mutation operators during image distribution.
CONTACT K. Venkatraman [email protected] Anna University, Chennai 600025, India
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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(ii) The novel contribution is the usage of stegano-graphic hybrid encryption which is adopted in cluster-based cloud environment to improve the security of images.
(iii) Here, the security is ensured by GA and the BA is utilized to reduce the computations involved in encrypting and decrypting the message.
The outline of the paper is organized as follows: Section 2 provides the related works. Section 3 dis-cusses the proposed hybrid algorithm and the stages of encryption. Section4evaluates the performance of the proposed algorithm. Section6concludes the work.
2. Related work
To ensure confidentiality or privacy and to make reli-ability on storage data, various mechanisms are pro-posed by the researchers. In [9], a novel authentication scheme is used to combine text and graphical passwords for better access control. The first round of graphi-cal authentication is a recognition technique and the second round is a recall technique. A next step is a behavioural study of the user and this approach offers highly secured systems in real time. In [10], an implicit password authentication system is used, where authen-tication is presented to the user. If the user “clicks” the grid-of-interest compared with the server, consumer and user are authenticated for using the services. No password information is exchanged between the client and the server. Since the authentication information is conveyed implicitly, this system tolerates shoulder surf-ing and screen dump attack. The main advantage lies in creating a better authentication space with a large collection of images to avoid short repeating cycles. Image encryption schemes meet the demand for real-time secure image transmission over the Internet [11]. The security of the digital image is important due to the rapid evolution of the Internet.
Homomorphic encryption [12] is a cryptographic tool used widely for improved security in cloud com-puting. This makes the cloud to operate on specific computation like encryption, ciphertext generation, and decryption. It provides privacy and security to the cloud outsourced data and storage. In [13], homo-morphic encryption is used to encode the image with direct operation. It suffers mainly from resource con-straints since the encryption is performed mostly on the client side with high computational overhead. Proxy re-encryption [14] secures the data sharing in cloud and delegates the capability of proxy encryption. The re-encrypt is taken place by the re-re-encryption key. Time-proxy re-encryption scheme [15] does not allow the client to encrypt the data and this avoids computational limitations during the use of resource-based constraint. Secure multiparty computation [16] enables different
parties for computing on the same function and main-tains the inputs, private. The other schemes related to secure multiparty computation in image processing is seen in [17,18]. However, it does not fit with thin clients, since parties involve in computational overhead due to symmetry property. Convectional cryptographic encryption models are proposed in [19] to encrypt the plain image before sending it over the cloud. It includes lossy image compression [20] with compressive sens-ing, template matching [21], image masking, and split-ting. Steganography technique in [22] stores the images in the cloud. It could be concluded that there are sev-eral techniques available to encrypt the data storage in a cloud environment using proxy re- and homomorphic encryption.
However, the proposed technique does not fit with the above technique, since the operation involves the encryption of images inside the cluster cloud network and further the resource is user limited. The secure multiparty computing can be used in such a hybrid cloud; however, the cost of operation at the client side is still expensive. Here, many steganography techniques [23,24] for image encryption is studied, but adoption in the cloud environment [20] is still less. The studies failed to report the hiding of images during its distribu-tion over the cloud. Computadistribu-tion on such steganogra-phy image is not available in conventional literature.
3. Proposed algorithm
The proposed method is intended to provide proper authentication of images in the hybrid cluster cloud system. The high-level security is attained by using a dual encrypting algorithm or SSIA algorithm. This algorithm uses blowfish technique to encrypt the
image information and then the genetic operators like crossover and mutation is used to encrypt the encrypted parent individuals. The subkeys of the blow-fish are stored in the cloud storage which avoids unau-thorized access. This method avoids the selection of random individuals to encrypt the images since it is dif-ficult to decrypt the images by the third party. However, a random number is utilized for the following purposes: (1) to encrypt the plain text with xor operation and (2) to reduce the total rounds in the blowfish algorithm.
Initially, a new random number is generated by the selection of a new random number by genetic algorithm from the whole set of population. The random number generated is of 64 bits and it checks for minimum five ones in the least significant bits of the random number, usually 16 bits. The generation of the random number is shown in Figure1. Depending on the position of the ones in random number i.e. least significant 16 bits, the function F is executed. If the least significant 16 bits have zeros then the rounds will not be executed. Now, check the conditionPilesser than 16, since the output of
Nis 16. When the condition is true, the loop goes from
the initial condition of generating population, on the contrary, the random number obtained from f is stored. The operation of two-tier encryption by the blowfish and genetic algorithm is shown in Figure2. This leads to severe variation in function F during the process of executing the encryption and decryption function. This method resists the attacks at any stages or rounds since it executes five rounds using blowfish and the final rounds are done genetically. The proposed method thus has an integrated SSIA algorithm to provide high-level security to the images in the private cloud.
3.1. Blowfish algorithm
Blowfish algorithm is a symmetric operation, which is used for both encryption and data protection in high-end system. The algorithm operates on a key length of 32 bits, variable in its manner and it extends till 448 bits. This is considered supreme for protecting the data in the cloud environment.
This is a 16-round Feistel cipher with 4 key-dependent S-boxes. The scheduling procedure of key is done by initializing both the S-box and P-box and the
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Figure 3.Proposed system architecture.
values are obtained from hexadecimal digits ofpiwith an improper pattern, respectively. The P-box entries are XORed with the secret key to improve the encryption process. When the value ofiis lesser than 16, the pro-cess gets back to the initialization step, wheref (i) is the considered one to acquirexLandxR, otherwise onlyxL
is computed. On the other hand, when the value ofiis greater than 16, then the process shifts to final opera-tions like swapping, second level encryption, crossover, mutate, and combination of child individuals.
From Figure3, the matrix value of each pixel value is obtained. The matrix values are used to obtain the RGB plane and it is dissected vertically into two halves,
xRandxL. Each half is of 32 bits and the keys of two halves are encrypted individually using a user-defined key. The encrypted planes are then retrieved by con-catenating vertically the two halves, xR and xL. The encrypted RGB planes are then merged together for constructing the final image, which is passed to the next stage of genetic operation.
3.1.1. Operation of blowfish algorithm
The entire blowfish operation takes place under 16 rounds. Initially, a random number is generated and then the given input image of 64-bit element is gen-erated. Then, the random number is XORed with the input data. Thexis divided into two equal halves, each of 32 bit size, namely,xL,xR.
3.2. Genetic algorithm
Genetic algorithm searches the individuals based on the probability and it solves the problem associated
with optimization. This paper assigns the individuals to encrypt the given images using crossover and muta-tion operators. Also, the selecmuta-tion of individuals for random numbers is based on natural selection proce-dure. The main steps of the proposed genetic model have initial random number generation and assessment of individuals, obtained from the blowfish algorithm. It involves computing parent after selection from blowfish and production of offspring. Then, the child is allowed to mutate and next generation is chosen.
The generated chromosomes or random number from the entire set of population has a gene of dif-ferent variants. Then, the suitable fitness function is assigned to assess the chromosome since it is a natural selection process, the higher fitness chromosome indi-viduals are chosen for the next generation and then it is XORed with the input values. Finally, it produces chil-dren and proved to be fittest in terms of the initial fitness function.
The main aim is to find the optimal value of such element. Here, each chromosome has genes that are referred to as variables or element. Due to the natural selection process, the chromosome of parents competes with each other, depending on the fitness function. The higher compatibility objective function reproduces with higher probabilities based on following equation 1,
P(i)= Fitness(i)
iFitness(i)
(1)
Theith probability of chromosome is denoted asP, with fitness value of the chromosome is denoted byFitness. Finally, the child chromosomes from the two parent genes are shown in equation 2, 3,
xL=αP1+(1−β)P2 (2)
xR=(1−α)P1+βP2 (3) wherexLandxRare the child chromosomes andP1and
P2are the parent chromosomes. Here,αandβare the constant values, where the values lie between 0 and 1.
During mutation, the parent chromosome from the blowfish stage of random position interchanges, in terms of its bits. The main aim is to increase the fit-ness of the chromosome, so that when the chromosome stay fit when the image pixels are shuffles and the pixel correlation stays least.
3.3. Blowfish image decryption
The output of the combined bits are given as an input to the blowfish decryption, which operated in the reverse way of the encryption mode; however, the order of the rounds is operated in a reverse way.
Appendix
Generate random number,R
SendRto an input image to generate 64-bit element,x
DividexintoxLandxR,
Fori=1–16 //Check Figure 3 Iff(i)=1 //Check Figure 3
xL=xLxorPi //P-array is XORed with keybits,xL//Piis theP-array withithiterations xR=F(xL)xorxR // the function ofxLis XORed withxR
//The function F is calcualted as follows, DividexLinto four quartersa,b,c,d.
Calculate F(xL)=((S1,a+S2,bmod 232) xor S3,c)+S4,dmod 232 Else
xL=xLxorPi //P-array is XORed with keybits,xL//Piis theP-array withithiterations
End
Swap the 23 bitsxLandxR
Swap the 23 bitsxLandxR(Undo the last swap) //The new output 1 and 2 is obtained after swapping Then,
xR=xRxorP17 //Encrypt again the swapped outputs with modified subkeyP17
xL=xLxorP18 //Encrypt again the swapped outputs with modified subkeyP18 //New output is obtained
Crossover the child (xR, xL) //Genetic crossover is carried out on new outputs
Mutate the Parent (R,L) //The parent individuals are mutated to obtain child individuals Recombine (L,R) //Recombination of child individuals takes place
End
4. Proposed cloud stegnographic image authentication model
The proposed security model on cluster private cloud shares the image data in a dynamic way over the untrusted cloud. The proposed model preserves the data and identity. The unauthorized access of the data is prevented using hybrid stegnographic model with improved security in the private cloud. This is attained using the edge detection technique, which reduces fur-ther the key size and computational complexity. The data is accessed by the end user and it is not authorized to be used by the other data owners. The proposed data cloud model is shown in Figure3.
The proposed SSIA algorithm is intended to share the user data in clusters and provides privacy and secu-rity when it is been distributed over the cloud. SSIA algorithm had embedded image like image steganog-raphy; however, an edge detection method is used for edge detection, where the user data is hidden. Prior to hiding the cover image data, confidentiality method is used to encrypt the data using the above blowfish-genetic operator.
The ciphertext while encrypting an image depends on the key with high quality, perfect selection, and design, since the adjacent pixel arrangement in the transposition cipher is disturbed and rearrangement pattern selects the encryption quality. The key is used to derive the rearrangement pattern and the quality of encryption is improved with the proper key. The rear-rangement ofn-pixel blocks is done using a key withn
numbers ofn! patterns. The genetic algorithm is used to improve the search pattern for obtaining the optimal value.
In the proposed method, the best chromosome is affected heavily by the image pixel correlation. Here, the chromosome of size 16 is considered and the elements are integers, which indicates the updated position by
the image pixel with a block size of 16. Each integer of the element in the chromosome is said to exist between the values of 1 and 16. The integerkon the chromo-some (left) is considered as the new position, which is been assigned to pixel (k) inside the string of chromo-some. The fitness function is used to evaluate how fit the chromosomes is, which is given in equation 1.
The proposed method uses the initial pre-processing operation to improve image encryption in an effective way. The image blocks are applied with the scram-bling technique for pre-encryption process. Then, the image is broken down into small square blocks with 90 deg phase shift along the counter-clockwise direc-tion, which has been flipped upside down. Finally, the transformed image is obtained from such shuffled blocks using the chaotic generator.
5. Experimental results
The proposed encryption algorithm is applied on Lena image. To check the effectiveness of the proposed SSIA algorithm, the correlation between the pixel blocks is tested. Initially, the pre-processing is carried out to obtain the transformed image. Then, RGB out-put images using blowfish and genetic algorithm is encrypted and analysed the performance of security over private cloud clusters. The results of encryption are shown in Figure4.
5.1. NPCR and UACI
The proposed method uses double-layer encryption model, which is evaluated using pixels changing rate (NPCR), unified average changing intensity (UACI), and correlation co-efficient (CC).
The parameters, NPCR and UACI, have used secu-rity analysis during the process of image encryption.
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Figure 4.Encryption results.
NPCR checks the pixel’s absolute number and UACI checks the average difference between the cipher images (two pairs). The encryption technique is proved efficient if the value of NPCR is high and the value of UACI is low. NPCR= i,jD(i,j) T ×100% (4) UACI= i,jC1(i,j)−C2(i,j) FT ×100% (5)
where C1 andC2 are the cipher images andD is the bipolar array,Tis the total pixels in cipher image, and
F is the largest pixel value in cipher image. PVAL is the qualitative NPCR/UACI and SCR is the quantitative NPCR/UACI.
The values of the NPCR and UACI for the encryp-tion using blowfish algorithm is shown in Tables1and
2. Similarly, the NPCR and UACI for the encryption using the proposed algorithm is shown in Tables 3
and4.
5.2. Correlation coefficient
The correlation between the Lena images for encryp-tion and decrypted image is shown in Table2. From the results, it is seen that the proposed algorithm attains a better correlation than the blowfish alone. This is due to the fact that the proposed algorithm selects optimally the image pixels using a genetic algorithm.
5.3. Execution time
The execution time of the proposed system is compared with other conventional methods, shown in Table3.
From the table, it is concluded that the proposed method attains a lesser execution time for encryption and decryption than the existing techniques like adap-tive LSB substitution [25], adaptive neural networks [26], and iterative magic matrix encryption algorithm [25]. The simpler computation process attains a lesser execution time in the proposed system than the other methods, where all the three methods possess higher computational process.
5.4. PSNR
PSNR is used to test the difference between the original and processed image and it is considered as a main cri-terion to identify the image quality. The stego quality of the images under steganographic method with various embedding rate is estimated using the messages, which is embedded into 1000 cover and 1000 stego images. The PSNR results between cover and stego images is
Table 1.NPCR color components of the proposed system with blowfish algorithm.
NPCR color components
Techniques Evaluation parameter R G B Entire image NCPR value of blowfish algorithm SCR 0.99632 0.9956 0.998291 0.99674
PVAL 0.59889 0.308192 0.987914 0.876356 NCPR value of proposed algorithm SCR 0.34917 0.340178 0.33929 0.34288
PVAL 8.45E-05 0.133793 0.208028 1.12E-04 UACI value of blowfish algorithm SCR 0.997314 0.993408 0.996093 0.995605 PVAL 0.500000 0.691807 0.401104 0.557494 UACI value of proposed algorithm SCR 0.341224 0.303878 0.282368 0.309157 PVAL 0.001327 0.001327 0.001327 1.12E-04
Table 2.Correlation coefficient.
Image R G B Entire image Blowfish Encrypted 0.029 −0.039 0.015 −0.006 Decrypted 1 1 1 1 Proposed algorithm Encrypted −0.0179 −0.0129 −0.0014 −0.0009 Decrypted 1 1 1 1 shown in Figure 4. PSNR=10log10 (2d−1)2 mse
wheredis the number of bits for representing the sam-ples. Further, the mse is the mean ofmk values over entire image blocks:
mse= 1 N N k=1 mk
andmk is the mean square error between the blocks, which is given by mk= 1 n n i=1 (xki −yki)2 wherek=1, 2,. . . ,N.
From the results of Table 4, it is found that the proposed system attains better results than the conven-tional methods. This is due to the fact that the least significant bits have changed in the cover image. The PSNR of the proposed method under varying embed-ding rates are always greater than 40 decibels and that lies in the acceptable range.
6. Conclusion
Security in hybrid cluster cloud environment is an important concern in this paper, which is achieved dynamically by hybrid steganographic model in the pri-vate cloud. The issues related to security of images in the cloud is carefully handled by the proposed system using dual-type encryption model. This model performs fast and ensures better security with hybrid blowfish and genetic operator model. The selection of chromosomes plays a major part, which is attained in an optimized way than the other cryptographic algorithm over the cloud environment. From the results, it is seen that the proposed method attains better security than the con-ventional model and ensures fast processing capability. The risk of providing security over dynamic cluster-based private cloud is attained carefully with better operation, which does not increase much the compu-tational complexity of the cloud system. Further, the
Table 3.Execution time (in seconds) of the proposed method with other conventional methods.
Adaptive LSB substitution [25]
Adaptive neural networks [26]
Iterative magic matrix
encryption algorithm [25] Proposed
Encryption time 124.54 112.14 107.25 86.012
Decryption time 125.03 112.62 106.24 86.547
Table 4.PSNR of the proposed method with other conventional methods.
Embedding rate LSB replacement Adaptive LSB substitution [25] Adaptive neural networks [26]
Iterative magic matrix
encryption algorithm [25] Proposed method
0.25 54 52.5 50.51 48.54 45 0.5 50 48.21 47.28 44.25 42 0.75 48 47.24 46.85 44.28 40 1 45 44.84 42.32 40.32 38 1.25 44 43.65 41.21 40.57 38.43 1.5 43.7 42.15 40.04 38.65 38.1 1.75 43.65 42.84 39.84 38.54 35.5 2 43.1 42.58 38.62 36.45 35.1
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work can be improved with the use of proxy-encryption with the highest entropy and least correlation than this work.
Disclosure statement
No potential conflict of interest was reported by the authors.
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